Predictive maintenance for a mold factory: from fix-after-break to early warning
Client Profile
Pain Points
Sudden failures halt production
One injection machine down stops the whole line, costing tens of thousands.
Maintenance depends on veterans
Only a few senior workers know machine health, and that knowledge walks when they leave.
Rough upkeep plans
Fixed-interval upkeep both over- and under-serves equipment.
Live Demo
How We Built It
Sensor data collection
Vibration, temperature and current sensors on key equipment stream real-time data.
Failure prediction
Models detect degradation trends and warn before failure.
Smart upkeep plans
Dynamic maintenance and spare-part reminders based on actual condition.
Not betting on one AI — combining the right few
For a real business scenario, we don't lock in a single technical path up front. We evaluate candidate paths on the same benchmark, then design a hybrid so each path does what it's best at.
General LLM API
Public-cloud models such as GPT-4o / Qwen / DeepSeek
- ✓Out-of-the-box, minimal integration cost, cheap to start
- ✕Limited generalization to vertical domains, unstable field-level accuracy, separate compliance design
Industry fine-tuned model
Domain fine-tuning of open-source models on industry data
- ✓Grasps industry semantics, much higher accuracy, private deployment
- ✕Needs labeled data plus ongoing training and upkeep
RAG + rule engine
Retrieval-augmented knowledge plus business rule checks
- ✓Highly targeted, explainable, best latency and cost
- ✕Requires continuous data-loop investment
Fusion, not selection
What we deliver is not a single model but a fused pipeline of primary path + semantic correction + graceful fallback: routine cases are handled by the industry model in seconds; low-confidence fields are re-understood by an LLM; on service anomalies the system degrades to a general API and flags manual review — achieving accuracy, cost, latency and controllability at once.
An explainable, degradable, evolvable AI pipeline
We treat AI engineering as a pipeline, not a black box: every step has clear responsibilities, inputs/outputs and fallbacks. The five-step pipeline plus four-layer architecture below is the technical skeleton.
Ingestion & access layer
Multi-channel access with unified auth, idempotency and masking
AI inference layer
Multi-path inference plus a confidence router switching by features
Business orchestration layer
Connecting results to real business flows with rule checks and closure
Data loop layer
Every online error flows back; models iterate monthly
Tech Stack
Delivery Process
Equipment assessment
Ranked 20 machines and set monitoring priorities.
Data collection
Deployed sensors and edge gateways to build baseline data.
Training
Trained degradation and failure-prediction models.
Pilot
Validated warning accuracy on 3 key machines.
Rollout
Covered all equipment with a maintenance loop.
Before vs After
“Now my phone warns me before a machine breaks, so we no longer get called back at midnight.”
Where It Fits
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